Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks
While most previous work has focused on different pretraining objectives and architectures for transfer learning, we ask how to best adapt the pretrained model to a given target task. We focus on…
Evaluating Gender Bias in Machine Translation
We present the first challenge set and evaluation protocol for the analysis of gender bias in machine translation (MT). Our approach uses two recent coreference resolution datasets composed of…
ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing
Despite recent advances in natural language processing, many statistical models for processing text perform extremely poorly under domain shift. Processing biomedical and clinical text is a…
Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing
Research on parsing language to SQL has largely ignored the structure of the database (DB) schema, either because the DB was very simple, or because it was observed at both training and test time.…
MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension
A large number of reading comprehension (RC) datasets has been created recently, but little analysis has been done on whether they generalize to one another, and the extent to which existing…
Adaptive Hashing for Model Counting
Randomized hashing algorithms have seen recent success in providing bounds on the model count of a propositional formula. These methods repeatedly check the satisfiability of a formula subject to…
CEDR: Contextualized Embeddings for Document Ranking
Although considerable attention has been given to neural ranking architectures recently, far less attention has been paid to the term representations that are used as input to these models. In this…
Ontology-Aware Clinical Abstractive Summarization
Automatically generating accurate summaries from clinical reports could save a clinician's time, improve summary coverage, and reduce errors. We propose a sequence-to-sequence abstractive…
Exploiting Explicit Paths for Multi-hop Reading Comprehension
We propose a novel, path-based reasoning approach for the multi-hop reading comprehension task where a system needs to combine facts from multiple passages to answer a question. Although inspired by…
Quantifying Sex Bias in Clinical Studies at Scale With Automated Data Extraction
Importance: Analyses of female representation in clinical studies have been limited in scope and scale. Objective: To perform a large-scale analysis of global enrollment sex bias in clinical…